CN111047104B - Energy consumption optimization method of grinding system - Google Patents
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- 238000000034 method Methods 0.000 title claims abstract description 126
- 238000005457 optimization Methods 0.000 title claims abstract description 53
- 238000005265 energy consumption Methods 0.000 title claims abstract description 46
- 239000000463 material Substances 0.000 claims description 96
- 239000002994 raw material Substances 0.000 claims description 40
- 239000000843 powder Substances 0.000 claims description 39
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 26
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Classifications
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B02—CRUSHING, PULVERISING, OR DISINTEGRATING; PREPARATORY TREATMENT OF GRAIN FOR MILLING
- B02C—CRUSHING, PULVERISING, OR DISINTEGRATING IN GENERAL; MILLING GRAIN
- B02C25/00—Control arrangements specially adapted for crushing or disintegrating
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/04—Manufacturing
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/30—Computing systems specially adapted for manufacturing
Abstract
The invention relates to a grinding system energy consumption optimization method based on an optimization model. The method comprises the steps of firstly dividing a grinding system into a plurality of different subunits, establishing corresponding models on the subunits, and obtaining an optimized model of the grinding system. On the basis, the optimization problem between the energy consumption and the running state of the grinding system is established, and finally, the energy consumption of the grinding system is optimized by optimizing the state set value of the grinding system. The method can effectively reduce the comprehensive energy consumption level of the main motor and the fan in the grinding system.
Description
Technical Field
The invention relates to the field of parameter optimization of a grinding system, in particular to the field of optimizing energy consumption of a mill through a mathematical model.
Background
The production process of cement requires crushing and grinding raw materials to carry out subsequent processing, and the traditional ball mill is gradually replaced by a vertical roller mill due to high energy consumption, large occupied area and low production efficiency.
The energy consumption of the whole cement production line is mainly divided into electricity consumption and coal consumption, the coal consumption mainly depends on the design and operation of a firing system, and the electricity consumption is distributed in the whole plant. Wherein the raw material grinding and the cement clinker grinding systems respectively account for 25% -30% of the power consumption of the whole factory. Where the system design is established, its power consumption is primarily dependent on the process operating conditions. The reasonable process target state and the stable control scheme can effectively reduce the comprehensive energy consumption index of the process.
The energy consumption optimization of the current cement grinding process is mainly realized by adopting a manual experience method, and through continuous manual attempts, the operation experience is accumulated in the attempted process. The energy consumption optimization achieved by the method mainly depends on experience levels of operators, and operator operation results of different experience levels can be greatly different. On the other hand, when a DCS autonomous loop is put into service, the optimization problem is mainly to consider the optimization of the loop set point, which is the lack of experience knowledge of the operator. In addition, under the condition of manual operation, the running state of the process cannot be quickly captured to take corresponding measures, so that the time for optimizing the process is missed.
Based on the above consideration, the invention firstly establishes a steady state model of the raw material grinding process, determines steady state prediction output of the model according to the operation variable and the raw material property of the current grinding system, compares the steady state prediction output with the actual process variable, and uses the steady state prediction output as the basis for online correction of the raw material property. The self-adaptive strategy can effectively consider the raw material property in the model, and on the basis, the optimal setting parameters are determined under given constraint by an optimization algorithm, so that the comprehensive energy consumption of the grinding system is reduced.
Disclosure of Invention
The invention provides a method for optimizing power consumption of a grinding system and constructing a power consumption model of the grinding system, wherein the grinding system comprises a grinding disc area, a wind ring area, a grinding cavity area and a plurality of units of a powder concentrator area, the grinding disc area comprises a grinding disc center area and a grinding disc grinding area, and the method comprises the following steps:
1) Collecting material parameters and system parameters of a grinding system,
2) Respectively constructing unit models for the millstone area, the wind ring area, the milling cavity area and/or the powder concentrator area according to the law of conservation of mass, preprocessing the data acquired in the step 1),
3) Constructing an energy model of the grinding system according to the law of conservation of energy, constructing an energy consumption model of the grinding system, and
4) Constructing an optimization model containing planning conditions using the models of 2) -3), and
optionally 5) solving the optimization model according to the planning conditions to obtain the set parameters of the grinding system,
optionally 6) setting parameters of the grinding system according to the result of step 5).
In one or more embodiments, the material parameter is selected from the group consisting of feed amount, slag discharge amount, screen margin, and mill inlet temperature. Preferably, the screen surplus is 80um screen surplus of raw meal.
In one or more embodiments, the system parameter is selected from grinding pressure, ventilation, powder concentrator speed, main motor current, and fan current.
In one or more embodiments, the method further comprises the steps of: and carrying out online correction on the raw materials through the difference between the prediction result of the optimization model and the actual process variable.
In one or more embodiments, the unit model includes a millstone center region model, a millstone grinding region model, a wind ring region model, a mill cavity region model, and/or a powder concentrator region model.
In one or more embodiments, the abrasive disc center region model is an abrasive disc center region crushing model.
In one or more embodiments, the disc grinding zone model is a disc grinding zone quality model.
In one or more embodiments, the wind ring area model is a wind ring area powder model.
In one or more embodiments, the grinding chamber region model is a grinding chamber region powder model.
In one or more embodiments, the powder concentrator region model is a powder concentrator region powder concentrator model.
In one or more embodiments, the center region model of the abrasive disc is as shown in formula (1-1)
Wherein the method comprises the steps ofRepresenting the transfer flow of the ith grade solid phase material between the areas; />Indicating the mass of material provided by the feed, +.>Representing the mass of material transferred from the center of the disc to the disc grinding zone.
In one or more embodiments of the present invention,as shown in formula (1-2) and/or +.>As shown in (1-3):
wherein the method comprises the steps ofRepresenting the component proportion of the ith grade material of the raw material; q (Q) F Indicating the feed amount of the raw material; />And the like, representing the retention amount of the ith grade material in each area; τ BG Indicating the time required for the transfer of material from zone B to zone G.
In one or more embodiments, the millstone grinding area model is as shown in formulas (1-4)
Wherein the method comprises the steps ofIndicating the mass of the i-th grade material broken, < > and>indicating the mass of the material converted from large particle size reduction to grade i, < >>Representation transition from G region to S I Flow of the ith grade material of the zone.
In one or more embodiments of the present invention,given by formula (1-5):
wherein the method comprises the steps ofA threshold value representing the mass of material in the grinding zone, b G The rate at which material leaves the grinding zone and falls into the zone of the wind ring is indicated. />It is defined that when the mass is below this value, material cannot leave the zone. In one or more embodiments, <' > a ∈>The size of the (c) is highly influenced by a material blocking ring in the grinding system. In one or more embodiments, the volume of material in the grinding zone is calibrated and then converted to mass.
In one or more embodiments, the parameter S and the parameter b in the formula (1-4) are expressed as follows (1-6) - (1-7)
b i,j ＝B i-1,j -B i,j (1-7)
Wherein d is i Represents the upper limit of the particle size of the ith grade of material; h is a g And h g0 The Hardgrove hardness and the reference hardness of the feedstock are indicated; p (P) G And P 0 Represents the grinding pressure and the reference grinding pressure; k (K) s Alpha, beta, phi, gamma, delta are model parameters. Preferably, the parameters are as shown in table 1.
In one or more embodiments, the wind ring region model is as shown in formulas (1-8):
wherein the method comprises the steps ofThe calculation of (2) is shown as the formula (1-9):
wherein the method comprises the steps ofIndicated at S I The particle size of the material in the region where drag force is balanced with gravity; phi (·) represents a standard normal distribution function; />Is a distribution parameter.
In one or more embodiments of the present invention,particles of a size that are 50% likely to fall into the grinding chamber.
In one or more embodiments, the grinding chamber region model is as shown in formulas (1-10):
wherein the method comprises the steps ofThe material flow rates transferred from the grinding chamber suspension area to the powder concentrator area, the grinding disc center area and the grinding disc grinding area are respectively shown.
In one or more embodiments of the present invention,as shown in formulas (1-11) - (1-13), respectively,
in the above formula, the definitions of the variables and functions are the same as or similar to those of the formulas (1 to 9).
In one or more embodiments, the powder concentrator region model is as shown in formulas (1-14):
wherein the method comprises the steps ofRepresenting material returning from the classifier region to the center of the mill.
In one or more embodiments of the present invention,as shown in the formula (1-15):
in the above formula, the definitions of the variables and functions are the same as or similar to those of the formulas (1 to 9).
In one or more embodiments, the energy model entering the grinding system is as shown in formulas (1-16):
Q in ＝Q air c a T in +Q m [(1-ω F )c m +ω F c w ]T e +H g (1-16)
the energy model leaving the grinding system is shown as (1-17):
wherein omega F Is the water content of the raw materials; c a Is the specific heat capacity of the gas; q (Q) air Representing inlet gas flow; c m Represents the specific heat of the solid; c w Represents the specific heat of water; t (T) e Representing ambient temperature; h g ，H loss Respectively representing grinding heat generation and heat dissipation of a grinding system; h is a v Represents the heat of vaporization of water; t (T) out Representing the outlet temperature of the grinding system;the mass flow of the finished product of the mill is shown; omega C Indicating the water content of the material of the finished product.
In one or more embodiments, the energy consumption model of the pulverizing system is shown in formulas (1-27) and (1-28):
wherein Q is m Representing the power consumption of the driving motor; q (Q) f The power consumption of the circulating fan is represented; u (U) m ，U f Driving voltage for each motor; k (k) m ，b m ，k f ，b f Respectively model parameters. In one or more embodiments, inflow may be obtained by regression from historical or experimental data. The method comprises the step of carrying out linear regression according to the mass of materials on the grinding disc, the current of a main motor, the rotating speed of a circulating fan and the current of the circulating fan according to historical data.
In one or more embodiments, an optimization model is constructed by optimizing the energy consumption index of the steady-state model established above. In one or more embodiments, the optimization model is represented by the following formulas (1-29) - (1-33):
minQ m +Q f (1-29)
Lb T ≤T out ≤Ub T (1-33)
wherein the method comprises the steps ofFormulas (1-29) are objective functions for minimizing energy consumption; the formula (1-30) shows that the slag discharge amount of the grinding system is in a constraint range; the formula (1-31) shows that the material quantity in the grinding area of the grinding system ensures that the grinding system does not generate severe vibration; the formula (1-32) shows that the 80 micron screen allowance of the raw meal at the outlet of the grinding system is in a reasonable range; the formulas (1-33) show that the water content of the raw meal represented by the outlet temperature of the grinding system reaches the expected index. Wherein M, M, d i And T out And Lb and Ub represent the lower and upper limits, respectively, of the corresponding superscript variable, as defined above.
In one or more embodiments, the systems or methods described herein further include a correlation model between the air volume and the fan speed or baffle opening. In one or more embodiments, the systems or methods described herein further include an empirical model of the relationship between the gas viscosity coefficient and the gas temperature.
In one or more embodiments, the planning conditions are independent relationships with respect to variables in the optimization model.
In one or more embodiments, the planning condition may be a function of a relationship between the grinding system energy consumption and the grinding system operating state. In one or more embodiments, the planning conditions include, but are not limited to, the amount of slag output of the grinding system being within a constraint range, the amount of material in the grinding region of the grinding system being such that the grinding system does not produce severe vibration, the 80 micron screen margin of the raw meal at the outlet of the grinding system being within a reasonable range, the water content of the raw meal being indicative of the outlet temperature of the grinding system reaching an expected index.
The methods or systems described herein also include online modification of the feedstock by optimizing the differences between the predicted results of the model and the actual process variables.
In one or more embodiments, the correction is a correction to a property of the feedstock, such as an online correction to a property parameter of the feedstock in the optimization model by a gradient descent, recursive least squares, or periodic offline assay correction.
In one or more embodiments, one or more physical parameters of the feedstock are modified. In one or more embodiments, the moisture content and/or Hardgrove hardness of the feedstock is modified.
In one or more embodiments, the water content ω of the feedstock F The correction method of (2) is shown as the formula (1-25)
Wherein alpha is ω Is a step size factor between 0 and 1,representing the partial derivative of the outlet temperature of the grinding system with respect to the moisture content of the raw material.
In one or more embodiments, the partial derivatives are obtained according to models (1-1) - (1-17) above. For example, the dynamic term of the model is first set to 0, the model is expressed in the form of an equation, and the result is obtained by using a chain rule of solving partial derivatives by using a hidden function.
In one or more embodiments, the Hardgroove hardness index h of the feedstock g The correction method of (2) is shown in the formula (1-26):
wherein alpha is h Is a step size factor between 0 and 1,the partial derivative of the mass retention of the material in the grinding zone with respect to the hardness of the material Hardgrove is shown.
In one or more embodiments, the partial derivatives are obtained synthetically according to the models (1-1) - (1-17) described above. For example, the dynamic term of the model is first set to 0, the model is expressed in the form of an equation, and the result is obtained by using a chain rule of solving partial derivatives by using a hidden function.
In one or more embodiments, the optimization solution method is selected from one or more of the following: gradient descent method, conjugate gradient method, newton method, quasi-Newton method, pattern search, etc. In a preferred embodiment, a pattern search method is used for optimization solution.
In addition, the invention also discloses an energy consumption optimizing system of the grinding system, which comprises a computer and a computer program running on the computer, wherein the computer program runs the method for optimizing the energy consumption of the grinding system in the previous embodiment on the computer.
The invention also discloses a computer readable storage medium storing a computer program, and the computer program stored on the storage medium executes the method for optimizing the energy consumption of the grinding system according to the previous embodiment after running.
The invention also provides a grinding system parameter setting method, the grinding system comprises a grinding disc area, an air ring area, a grinding cavity area and a plurality of units of a powder selecting machine area, the grinding disc area comprises a grinding disc center area and a grinding disc grinding area, and the method comprises the following steps:
1) Collecting material parameters and system parameters of a grinding system,
2) Optimizing set parameters of a grinding system using the model constructed by the method described herein, and
3) The grinding system is adjusted/set according to the optimized setting parameters.
The invention also provides a grinding system parameter setting system, which comprises the following modules:
a data acquisition module for acquiring material parameters and system parameters of the grinding system,
the unit model construction module respectively constructs a unit model for the millstone area, the wind ring area, the milling cavity area and/or the powder concentrator area according to the law of conservation of mass, performs pretreatment for the data of the data acquisition module,
a system model construction module for constructing a grinding system energy model according to the law of conservation of energy and constructing a grinding system energy consumption model,
an optimization model construction module that constructs an optimization model using the models constructed by the unit model construction module and the system model construction module,
an optimization model solving module for solving the optimization model according to the planning conditions to obtain the set parameters of the grinding system, and
and the parameter setting module is used for setting parameters of the grinding system according to the result of the optimization model solving module.
The invention also provides a grinding system parameter setting system, which comprises a computer and a computer program running on the computer, wherein the computer program runs the method on the computer.
The invention also discloses a computer readable storage medium storing a computer program, characterized in that the computer program stored on the storage medium executes the method as described above after running.
The invention has the beneficial effects that:
(1) The process variable with high sensitivity is used as feedback to correct the raw material property, so that the change of the raw material property can be estimated more accurately in real time, thereby accurately correcting the model and improving the estimation accuracy of the model.
(2) The statistical model is adopted to describe the gas-solid separation process to replace the traditional cut-off model, so that the method is more in line with the actual process characteristics.
(3) In a preferred embodiment of the present invention, a mode search method is adopted, and the current state is used as an initial point, so that the feasibility of the solution can be effectively ensured.
Drawings
Fig. 1 shows a flow chart of one embodiment of the energy consumption optimization method of the present invention.
Fig. 2 shows an exemplary grinding system configuration.
Fig. 3 shows an exemplary milling process flow diagram.
FIG. 4 shows a plot of the results of sensitivity analysis of a model of one embodiment, with the Hardgrove hardness of the feedstock on the abscissa.
Figure 5 shows a plot of the sensitivity analysis results of the model of one embodiment, with the water content of the feedstock on the abscissa.
Detailed Description
Fig. 1 shows a flow chart of one embodiment of the energy consumption optimization method of the present invention. According to the method, according to the mechanism analysis of the running process of the grinding system, unit models of all areas are built by dividing the grinding system into a plurality of areas, and then the unit models are synthesized to obtain an optimized model of the grinding system. On the basis, the optimization problem between the energy consumption of the grinding system and the running state of the grinding system is established, and finally, the energy consumption of the grinding system is optimized by optimizing the setting parameters of the grinding system. The energy consumption described herein is preferably electricity consumption.
The pulverizing system described herein may be various kinds of mills as long as the mill can be divided into a plurality of units of a millstone region, a wind ring region, a milling chamber region, and a powder concentrator region. In one or more embodiments, the mill is a vertical mill, referred to as a mill, including a vertical roller mill. The vertical mill is a grinding equipment, which integrates crushing, drying, grinding and grading transportation into a whole, and can grind the block, granular and powdery raw materials into the required powdery materials. The grinding system described herein can be used in the grinding production of cement clinker.
In the invention, the grinding system is divided into a plurality of units such as a grinding disc area, an air ring area, a grinding cavity area, a powder selecting machine area and the like, wherein the grinding disc area comprises a grinding disc center area and a grinding disc grinding area. The structure of the pulverizing system and the relationship between the material flow and the gas phase flow between the regions are shown in fig. 2. Wherein, the liquid crystal display device comprises a liquid crystal display device,and the like, represent the transfer flow of the i-th solid phase material between the areas. For example, a->Indicating the quality of the ith grade material transferred from zone B to zone G. As shown in fig. 2, after the raw material enters the center of the grinding disc, the raw material enters the grinding area of the grinding disc from the central area of the grinding disc; the material flow in the grinding area of the grinding disc enters the air ring area; one part of the material flow in the air ring area enters the grinding cavity area, and the other part enters the center area of the grinding disc; one part of the material flow in the grinding cavity area enters the powder concentrator area, and the other part enters the grinding disc center area and the grinding disc grinding area; one part of material flow in the powder concentrator area leaves the grinding system, and the other part enters the center area of the grinding disc; from the wind ring area, millThe material flow entering the central area of the millstone from the cavity area and the powder concentrator area enters the millstone grinding area again together with the raw materials.
Herein, "material", "mixed material" or "solid phase" means a substance transferred in each system of the pulverizing system. In one exemplary embodiment, the composition of the material includes, but is not limited to, limestone, sandstone, shale, iron powder. The invention divides the solid phase flow in the grinding system into N stages according to the particle size. The modeling of the invention is to build energy or mass conservation models for particles in a certain stage on the basis of the modeling. Other modeling approaches suitable for the present invention are known in the art.
According to the invention, the whole energy model and the energy consumption model of the grinding system are obtained by establishing corresponding models on the units, and the property parameters of raw materials in the models are corrected on line. On the basis, the model is solved to obtain the optimal setting state of the grinding system. Parameters in the models described below can be estimated from historical data and empirical knowledge of the grinding process.
The invention provides a power consumption optimization method of a grinding system, the grinding system comprises a grinding disc area, a wind ring area, a grinding cavity area and a plurality of units of a powder concentrator area, the grinding disc area comprises a grinding disc center area and a grinding disc grinding area, and the method comprises the following steps:
1) Collecting material parameters and system parameters of a grinding system,
2) Respectively constructing unit models for the millstone area, the wind ring area, the milling cavity area and/or the powder concentrator area according to the law of conservation of mass, preprocessing the data acquired in the step 1),
3) Constructing an energy model of the grinding system according to the law of conservation of energy, constructing an energy consumption model of the grinding system,
4) Constructing an optimization model containing planning conditions using the models of 2) -3), and
5) Solving the optimization model according to the planning conditions to obtain the set parameters of the grinding system,
6) Setting parameters of the grinding system according to the result of the step 5).
These steps will be described in detail below. It is to be understood that within the scope of the present invention, the above-described technical features of the present invention and technical features specifically described below (as embodiments or examples) may be combined with each other to constitute a preferred technical solution.
Step 1, collecting material parameters and system parameters of a grinding system
This step determines the material parameters and system parameters of the grinding system. The material parameters include feeding amount, slag discharge amount, 80um screen allowance of raw meal and inlet temperature of a mill. The system parameters include grinding pressure, ventilation quantity, powder concentrator rotation speed, main motor current and fan current.
Step 2, respectively constructing unit models for the millstone area, the wind ring area, the milling cavity area and/or the powder concentrator area according to the mass conservation law
The step utilizes the material flow rate of each area to build each unit model. The unit model comprises a millstone central area model, a millstone grinding area model, a wind ring area model, a milling cavity area model and/or a powder concentrator area model. In one or more embodiments, the abrasive disc center region model is an abrasive disc center region crushing model; the millstone grinding area model is a millstone grinding area quality model; the wind ring area model is a wind ring area powder selection model; the grinding cavity area model is a grinding cavity area powder selecting model; the powder concentrator region model is a powder concentrator region powder concentration model.
(1) The center area model of the millstone is shown as (1-1)
Wherein the method comprises the steps ofAs shown in figure 2, the transfer flow of the i-th solid phase material between the areas is shown; the mass of material supplied by the feed ∈>As shown in the formula (1-2); transfer from centre of grinding disc to grinding discMaterial quality in the grinding zone->As shown in (1-3):
wherein the method comprises the steps ofRepresenting the component proportion of the ith grade material of the raw material; q (Q) F Indicating the feed amount of the raw material; />And the like, representing the retention amount of the ith grade material in each area; τ BG Indicating the time required for the transfer of material from zone B to zone G.
(2) The grinding area model of the grinding disc is shown as (1-4)
Wherein the method comprises the steps ofIndicating the mass of the i-th grade material broken, < > and>indicating the mass of the material converted from large particle size reduction to grade i, < >>Representation transition from G region to S I The flow rate of the i-th stage material of the zone, the calculation of which is given by the formula (1-5):
wherein the method comprises the steps ofA threshold value representing the mass of material in the grinding zone, from which the material cannot leave when the mass is below this value, the size of which is affected by the height of the retaining ring in the grinding system. Specifically, due to the low gap rate of the materials in the grinding area, the volume of the materials in the grinding area can be calibrated and then converted into mass. Methods for converting mass by volume of material are well known in the art. b G The rate at which material leaves the grinding zone and falls into the zone of the wind ring is indicated.
Wherein the expressions of the parameter S and the parameter b in the formula (1-4) are as shown in the following (1-6) to (1-7)
b i,j ＝B i-1,j -B i,j (1-7)
Wherein d is i Represents the upper limit of the particle size of the ith grade of material; h is a g And h g0 The Hardgrove hardness and the reference hardness of the feedstock are indicated; p (P) G And P 0 Represents the grinding pressure and the reference grinding pressure; k (K) s Alpha, beta, phi, gamma, delta are model parameters. Preferably, the parameters are as shown in table 1.
(3) The wind ring area model is shown in the formula (1-8):
wherein the method comprises the steps ofThe calculation of (2) is shown as the formula (1-9):
wherein the method comprises the steps ofIndicated at S I The particle size of the material in the region where drag force and gravity balance, with 50% of the particles falling into the grinding chamber; phi (·) represents a standard normal distribution function; />Is a distribution parameter. The value of the distribution parameter is affected by the nature of the feedstock and methods of determining the distribution parameter based on the feedstock nature are known in the art. In the exemplary embodiments herein, +.>1.71.
(4) The grinding cavity area model is shown in the formula (1-10):
wherein the method comprises the steps ofThe material flow rates transferred from the mill chamber suspension region to the powder concentrator region, the mill center region, and the mill grinding region are shown in formulas (1-11) - (1-13), respectively.
In the above formula, the definitions of the variables and functions are the same as or similar to those of the formulas (1 to 9).
(5) The regional model of the powder concentrator is shown in the formula (1-14):
wherein the method comprises the steps ofRepresenting material returned from the powder concentrator region to the center of the mill, the calculation is shown in formulas (1-15):
in the above formula, the definitions of the variables and functions are the same as or similar to those of the formulas (1 to 9).
Step 33.1, constructing an energy model of the grinding system according to the law of conservation of energy,
the method comprises the step of constructing a grinding system energy model by using property data of materials entering and exiting a grinding system and a heat generation value of the grinding system through energy equality of the materials entering and exiting the grinding system.
The energy model entering the grinding system is shown as (1-16):
Q in ＝Q air c a T in +Q m [(1-ω F )c m +ω F c w ]T e +H g (1-16)
the energy model leaving the grinding system is shown as (1-17):
wherein omega F Is the water content of the raw materials; c a Is the specific heat capacity of the gas; q (Q) air Representing inlet gas flow; c m Represents the specific heat of the solid; c w Represents the specific heat of water; t (T) e Representing ambient temperature; h g ，H loss Respectively representing grinding heat generation and heat dissipation of a grinding system; h is a v Represents the heat of vaporization of water; t (T) out Representing the outlet temperature of the grinding system;the mass flow of the finished product of the mill is shown; omega C Indicating the water content of the material of the finished product.
3.2 building an energy consumption model of the grinding System
The method comprises the steps of constructing an energy consumption model of a grinding system by using the electricity consumption of a main driving motor and the electricity consumption of a circulating fan, wherein the energy consumption model is shown in formulas (1-27) and (1-28):
wherein Q is m Representing the power consumption of the driving motor; q (Q) f The power consumption of the circulating fan is represented; u (U) m ，U f Driving voltage for each motor; k (k) m ，b m ，k f ，b f Respectively model parameters. The method for acquiring the parameters is known in the art, and inflow can be acquired through regression through historical data or experimental data, namely, linear regression acquisition is performed according to the mass of materials on the grinding disc of the historical data, main motor current, rotating speed of a circulating fan and current of the circulating fan.
Step 4, constructing an optimization model by using the unit model and the system model
In the step, an optimization model is built by optimizing the energy consumption index of the built steady-state model. To minimize the energy consumption index, the optimization model is shown in the following formulas (1-29) - (1-33):
minQ m +Q f (1-29)
/>
Lb T ≤T out ≤Ub T (1-33)
wherein formula (1-29) is an objective function of minimizing energy consumption; the formula (1-30) shows that the slag discharge amount of the grinding system is in a constraint range; the formula (1-31) shows that the material quantity in the grinding area of the grinding system ensures that the grinding system does not generate severe vibration; the formula (1-32) shows that the 80 micron screen allowance of the raw meal at the outlet of the grinding system is in a reasonable range; the formulas (1-33) show that the water content of the raw meal represented by the outlet temperature of the grinding system reaches the expected index. Wherein M, M, d i And T out And Lb and Ub represent the lower and upper limits, respectively, of the corresponding superscript variable, as defined above.
It should be noted that in practical applications, the air volume may not be detected online, and may need to be converted from other variables (such as the fan rotation speed or the baffle opening). Therefore, the invention can also comprise a correlation model between the air quantity and the fan rotating speed or the baffle opening. Furthermore, in order to ensure accuracy of the model, in some embodiments, the effect of temperature on the viscosity of the gas is also taken into account. The invention may also include empirical models of the relationship between the gas viscosity coefficient and the gas temperature.
And 5, solving the optimization model according to planning conditions to obtain set parameters of the grinding system.
The planning conditions are independent relationships about variables in the optimization model. The planning condition can be a relation function between the energy consumption of the grinding system and the running state of the grinding system, including but not limited to that the slag discharge amount of the grinding system is in a constraint range, the material amount in a grinding area of the grinding system is required to ensure that the grinding system does not generate severe vibration, the 80-micrometer screen allowance of raw meal at an outlet of the grinding system is in a reasonable range, and the water content of the raw meal represented by the outlet temperature of the grinding system reaches an expected index. These planning conditions depend on the production plan of the enterprise and the capacity of the equipment, such as the upper and lower limit constraints of the spouts, which are mainly dependent on the capacity of the vibrating discharge machine and the bucket. Therefore, methods for determining the above ranges or indices according to actual conditions are well known in the art. In an exemplary embodiment, the amount of spitting of the grinding system does not exceed 35% of the feed; the vibration of the grinding system is not more than 1.2mm/s; the 80 micron screen allowance of the raw meal at the outlet of the grinding system is within 18 percent; the outlet temperature of the grinding system is not lower than 70 ℃.
The optimization method described herein also includes online modification of the feedstock by differences between the predicted results of the optimization model and the actual process variables. The correction includes a correction for the properties of the feedstock, such as an on-line correction for the parameters of the properties of the feedstock in the optimized model by gradient descent, recursive least squares, or periodic off-line assay correction. All modifications to the parameters of the feedstock are within the scope of the invention. In one or more embodiments, one or more physical parameters of the feedstock are modified. In one or more embodiments, the moisture content and/or Hardgrove hardness of the feedstock is modified.
In one or more embodiments, the water content ω of the feedstock F The correction method of (2) is shown as the formula (1-25)
Wherein alpha is ω Is a step size factor between 0 and 1,representing the partial derivative of the outlet temperature of the grinding system with respect to the moisture content of the raw material, which can beObtained by integrating the above-mentioned models (1-1) - (1-17). For example, the dynamic term of the model is first set to 0, the model is expressed in the form of an equation, and the result is obtained by using a chain rule of solving partial derivatives by using a hidden function.
In one or more embodiments, the Hardgroove hardness index h of the feedstock g The correction method of (2) is shown in the formula (1-26):
wherein alpha is h Is a step size factor between 0 and 1,representing the partial derivative of the mass retention of the material in the grinding zone with respect to the hardness of the raw material Hardgrove, this can be obtained in combination according to the models (1-1) - (1-17) described above. For example, the dynamic term of the model is first set to 0, the model is expressed in the form of an equation, and the result is obtained by using a chain rule of solving partial derivatives by using a hidden function.
Sensitivity analysis of the inventive model to Hardgrove hardness and moisture content is shown in FIGS. 4 and 5. It can be seen from the graph that the sensitivity of the material quantity on the grinding disc to the hardness of the raw materials is larger, which means that the change of the material on the grinding disc can well represent the change of the hardness of the raw materials, and on the other hand, the sensitivity of the change quantity of the outlet temperature of the grinding machine to the water content of the raw materials is larger, which means that the change information of the outlet temperature of the grinding machine can represent the change of the water content of the raw materials. In summary, the process variable with high sensitivity is used as feedback to correct the raw material attribute, so that the change of the raw material property can be estimated more accurately in real time, thereby accurately correcting the model and improving the estimation accuracy of the model.
The process of obtaining state settings from model solutions is known in the art. In general, the optimal solution to the model is a nonlinear programming solution problem. General optimization solving methods such as gradient descent method, conjugate gradient method, newton method, quasi-newton method, pattern search and the like can be adopted. In a preferred embodiment, to ensure that the optimized result is always better than the original state, a pattern search method is used to perform the optimization solution, where the initial solution uses the current state value of the process object. The mode searching method searches the solution of the problem on the basis of the current solution until the maximum searching step number or the searching step length reaches the lower precision limit, and then the method exits, and the optimal solution is determined.
In addition, the invention also discloses an energy consumption optimizing system of the grinding system, which comprises a computer and a computer program running on the computer, wherein the computer program runs the method for optimizing the energy consumption of the grinding system in the previous embodiment on the computer.
The invention also discloses a computer readable storage medium storing a computer program, and the computer program stored on the storage medium executes the method for optimizing the energy consumption of the grinding system according to the previous embodiment after running.
While, for purposes of simplicity of explanation, the methodologies are shown and described as a series of acts, it is to be understood and appreciated that the methodologies are not limited by the order of acts, as some acts may, in accordance with one or more embodiments, occur in different orders and/or concurrently with other acts from that shown and described herein or not shown and described herein, as would be understood and appreciated by those skilled in the art.
Those of skill would further appreciate that the various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
The various illustrative logical blocks, modules, and circuits described in connection with the embodiments disclosed herein may be implemented or performed with a general purpose processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general purpose processor may be a microprocessor, but in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing devices, e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor such the processor can read information from, and write information to, the storage medium. In the alternative, the storage medium may be integral to the processor. The processor and the storage medium may reside in an ASIC. The ASIC may reside in a user terminal. In the alternative, the processor and the storage medium may reside as discrete components in a user terminal.
In one or more exemplary embodiments, the functions described may be implemented in hardware, software, firmware, or any combination thereof. If implemented in software as a computer program product, the functions may be stored on or transmitted over as one or more instructions or code on a computer-readable medium. Computer-readable media includes both computer storage media and communication media including any medium that facilitates transfer of a computer program from one place to another. A storage media may be any available media that can be accessed by a computer. By way of example, and not limitation, such computer-readable media can comprise RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer. Any connection is properly termed a computer-readable medium. For example, if the software is transmitted from a web site, server, or other remote source using a coaxial cable, fiber optic cable, twisted pair, digital Subscriber Line (DSL), or wireless technologies such as infrared, radio, and microwave, then the coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, radio, and microwave are included in the definition of medium. Disk (disk) and disc (disk) as used herein include Compact Disc (CD), laser disc, optical disc, digital Versatile Disc (DVD), floppy disk and blu-ray disc where disks (disk) usually reproduce data magnetically, while discs (disk) reproduce data optically with lasers. Combinations of the above should also be included within the scope of computer-readable media.
Examples
Step S01 is to perform sampling and averaging processing on the properties of the raw material for a plurality of times, and the sampling and averaging processing is used as an initial value of parameters in the model, including the water content of the raw material and the hardness index of the raw material.
Step S02 establishes each steady-state model and optimization model through equations shown in formulas (1-1) - (1-33), wherein the method also comprises the establishment of an empirical model such as a correlation model between air quantity and fan rotating speed or baffle opening, and a relation between a gas viscosity coefficient and gas temperature.
Step S03 estimates model parameters through historical data and experience knowledge. Estimating and solving model parameters by adopting a least square method: and taking model parameters as decision variables, taking the minimum sum of squares of deviations between actual process states and model prediction states as an optimization target, establishing an optimization problem, and solving by a known optimization method. The preferred values of the obtained estimation result are shown in the following table 1:
TABLE 1 estimation results of model parameters
Step S04, carrying out on-line correction on the water content of the raw material through a model on the steady-state deviation between the estimated value of the outlet temperature of the mill and the actual outlet temperature; and carrying out on-line correction on the hardness index of the raw material through the deviation between the estimated value of the material in the grinding area of the grinder and the material quality in the actual grinding area of the grinder.
Step S05, on the basis of the model establishment, the energy consumption index of the model is optimized through an optimization algorithm by considering the process production safety index and the quality index constraint. In a preferred embodiment, a mode search algorithm is used to optimize the mill air flow, the classifier speed and the mill inlet temperature.
The previous description of the disclosure is provided to enable any person skilled in the art to make or use the disclosure. Various modifications to the disclosure will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other variations without departing from the spirit or scope of the disclosure. Thus, the disclosure is not intended to be limited to the examples and designs described herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (13)
1. A method of optimizing power consumption of a grinding system and constructing a model of power consumption of a grinding system, the grinding system comprising a plurality of units of a grinding disc region, a wind ring region, a grinding chamber region, and a powder concentrator region, the grinding disc region comprising a grinding disc center region and a grinding disc grinding region, the method comprising the steps of:
1) Collecting material parameters and system parameters of a grinding system,
2) Respectively constructing a mass unit model for the millstone region, the wind ring region, the milling cavity region and/or the powder concentrator region, preprocessing the data acquired in the step 1),
3) Constructing an energy model and an energy consumption model of the grinding system, and
4) Constructing an optimization model containing planning conditions using the models of 2) -3), and
5) Solving the optimization model according to the planning conditions to obtain the set parameters of the grinding system, wherein,
the unit model comprises a millstone central area model, a millstone grinding area model, a wind ring area model, a millstone cavity area model and/or a powder concentrator area model, wherein,
the center area model of the millstone is shown as (1-1)
Wherein the method comprises the steps ofRepresenting the transfer flow of the ith grade solid phase material between the areas; />Indicating the mass of material provided by the feed,
the grinding area model of the grinding disc is shown as (1-4)
Wherein the method comprises the steps ofIndicating the mass of the i-th grade material broken, < > and>indicating the mass of the material converted from large particle size reduction to grade i, < >>Representing the flow of the ith grade material transferred from the disc grinding zone to the wind ring zone,
the wind ring area model is shown in the formula (1-8):
wherein the method comprises the steps ofThe calculation of (2) is shown as the formula (1-9):
wherein the method comprises the steps ofIndicating the particle size of the material in which drag forces are balanced with gravity in the region of the wind ring; phi (·) represents a standard normal distribution function; />Is a distribution parameter;
the grinding cavity area model is shown in the formula (1-10):
wherein the method comprises the steps ofRespectively represents the material flow rate transferred from the grinding cavity suspension area to the powder concentrator area, the grinding disc center area and the grinding disc grinding area,
the regional model of the powder concentrator is shown in the formula (1-14):
wherein the method comprises the steps ofIndicating selected powderThe machine area returns the material in the center of the millstone,
the energy model of the grinding system comprises an energy model entering the grinding system and an energy model leaving the grinding system, wherein,
the energy model entering the grinding system is shown as (1-16):
Q in ＝Q air c a T in +Q m [(1-ω F )c m +ω F c w ]T e +H g (1-16)
the energy model leaving the grinding system is shown as (1-17):
wherein omega F Is the water content of the raw materials; c a Is the specific heat capacity of the gas; q (Q) air Representing inlet gas flow; c m Represents the specific heat of the solid; c w Represents the specific heat of water; t (T) e Representing ambient temperature; h g ，H loss Respectively representing grinding heat generation and heat dissipation of a grinding system; h is a v Represents the heat of vaporization of water; t (T) out Representing the outlet temperature of the grinding system;the mass flow of the finished product of the mill is shown; omega C Representing the water content of the material of the finished product;
the energy consumption model of the grinding system is shown in formulas (1-27) and (1-28):
wherein Q is m Representing the power consumption of the driving motor; q (Q) f Indicating circulating fanIs not limited by the power consumption of the battery; u (U) m ，U f Driving voltage for each motor; k (k) m ，b m ，k f ，b f The parameters of the model are respectively the parameters of the model,
the optimization model is shown in the following formulas (1-29) - (1-33):
min Q m +Q f (1-29)
Lb T ≤T out ≤Ub T (1-33)
wherein formula (1-29) is an objective function of minimizing energy consumption; the formula (1-30) shows that the slag discharge amount of the grinding system is in a constraint range; the formula (1-31) shows that the material quantity in the grinding area of the grinding system ensures that the grinding system does not generate severe vibration; the formula (1-32) shows that the 80 micron screen allowance of the raw meal at the outlet of the grinding system is in a reasonable range; the formula (1-33) shows that the water content of the raw meal represented by the outlet temperature of the grinding system reaches the expected index;
the method further comprises online correction of the feedstock by a method selected from gradient descent, recursive least squares, or periodic offline assay correction by optimizing the difference between the predicted result of the model and the actual process variable, the correction being made by correcting one or more physical parameters of the feedstock, the physical parameters including the moisture content and/or Hardgrove hardness of the feedstock,
the water content omega of the raw material F The correction method of (2) is shown as the formula (1-25)
Wherein alpha is ω Is a step size factor between 0 and 1,represents the partial derivative of the outlet temperature of the grinding system to the water content of the raw materials,
hardgroove hardness index h of raw material g The correction method of (2) is shown in the formula (1-26):
wherein alpha is h Is a step size factor between 0 and 1,the partial derivative of the mass retention of the material in the grinding zone with respect to the hardness of the material Hardgrove is shown.
2. The method of claim 1, wherein,
as shown in formula (1-2) and/or +.>As shown in (1-3):
wherein the method comprises the steps ofRepresenting the component proportion of the ith grade material of the raw material; q (Q) F Indicating the feed amount of the raw material; />And the like, representing the retention amount of the ith grade material in each area; τ BG Representing the time required for material to transfer from the central region of the disc to the grinding region of the disc.
3. The method of claim 1, wherein,
given by formula (1-5):
wherein the method comprises the steps ofA threshold value representing the mass of material in the grinding zone, b G The rate at which material leaves the grinding zone and falls into the zone of the wind ring is indicated.
4. The method of claim 1, wherein,
parameter S in the formula (1-4) i And the parameter b expressions are shown in the following (1-6) - (1-7)
Wherein d is i Represents the upper limit of the particle size of the ith grade of material; h is a g And h g0 The Hardgrove hardness and the reference hardness of the feedstock are indicated; p (P) G And P 0 Represents the grinding pressure and the reference grinding pressure; k (K) s Alpha, beta, phi, gamma, delta are model parameters.
5. The method of claim 1, wherein,
as shown in formulas (1-11) - (1-13), respectively,
6. the method of claim 1, wherein,
as shown in the formula (1-15):
7. the method of claim 1, wherein the method of solving is selected from one or more of the following: gradient descent method, conjugate gradient method, newton method, quasi-Newton method, and pattern search.
8. The method of claim 1, wherein the material parameter is selected from the group consisting of feed rate, slag discharge rate, screen margin, and mill inlet temperature.
9. The method of claim 1 wherein the system parameter is selected from the group consisting of grinding pressure, ventilation, powder concentrator speed, main motor current and fan current.
10. A grinding system parameter setting method, the grinding system comprising a plurality of units of a grinding disc area, a wind ring area, a grinding cavity area and a powder concentrator area, the grinding disc area comprising a grinding disc center area and a grinding disc grinding area, the method comprising the steps of:
1) Collecting material parameters and system parameters of a grinding system,
2) Optimizing the setting parameters of the grinding system by adopting the model constructed by the method of any one of claims 1-9,
3) And adjusting the grinding system according to the optimized setting parameters.
11. An energy consumption optimizing system and a parameter setting system of a grinding system comprise the following modules:
a data acquisition module for acquiring material parameters and system parameters of the grinding system,
a unit model construction module which respectively constructs a quality unit model for the millstone area, the wind ring area, the millstone cavity area and/or the powder concentrator area, carries out pretreatment on the data of the data acquisition module,
a system model construction module for constructing a grinding system energy model according to the law of conservation of energy and constructing a grinding system energy consumption model,
an optimization model construction module that constructs an optimization model using the model constructed by the unit model construction module and the system model construction module, and
an optimization model solving module for solving the optimization model according to the planning conditions to obtain the set parameters of the grinding system,
each model is as defined in any one of claims 1 to 9.
12. An energy consumption optimization system and parameter setting system of a grinding system, comprising a computer and a computer program running on the computer, the computer program running the method of any one of claims 1-10 on the computer.
13. A computer readable storage medium storing a computer program, characterized in that the computer program stored on the storage medium performs the method of any one of claims 1-10 after execution.
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